no code implementations • ICML 2020 • Vikas Garg, Tommi Jaakkola
Our games take as input, e. g., UN resolution to be voted on, and map such contexts to initial strategies, player utilities, and interactions.
no code implementations • ICML 2020 • Vikas Garg, Tommi Jaakkola
Our games take as input, e. g., UN resolution to be voted on, and map such contexts to initial strategies, player utilities, and interactions.
no code implementations • ICML 2020 • Wengong Jin, Regina Barzilay, Tommi Jaakkola
These rationales are identified from molecules as substructures that are likely responsible for each property of interest.
no code implementations • 5 May 2024 • Ezra Erives, Bowen Jing, Tommi Jaakkola
Approximations in computing model likelihoods with continuous normalizing flows (CNFs) hinder the use of these models for importance sampling of Boltzmann distributions, where exact likelihoods are required.
2 code implementations • 28 Feb 2024 • Gabriele Corso, Arthur Deng, Benjamin Fry, Nicholas Polizzi, Regina Barzilay, Tommi Jaakkola
Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome.
1 code implementation • 8 Feb 2024 • Hannes Stark, Bowen Jing, Chenyu Wang, Gabriele Corso, Bonnie Berger, Regina Barzilay, Tommi Jaakkola
Further, we provide distilled Dirichlet flow matching, which enables one-step sequence generation with minimal performance hits, resulting in $O(L)$ speedups compared to autoregressive models.
1 code implementation • 7 Feb 2024 • Bowen Jing, Bonnie Berger, Tommi Jaakkola
When trained and evaluated on the PDB, our method provides a superior combination of precision and diversity compared to AlphaFold with MSA subsampling.
1 code implementation • 7 Feb 2024 • Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi Jaakkola
Our approach achieves state-of-the-art co-design performance while allowing the same multimodal model to be used for flexible generation of the sequence or structure.
1 code implementation • 2 Feb 2024 • Menghua Wu, Yujia Bao, Regina Barzilay, Tommi Jaakkola
Causal discovery, the task of inferring causal structure from data, promises to accelerate scientific research, inform policy making, and more.
1 code implementation • 10 Dec 2023 • Yujian Liu, Yang Zhang, Tommi Jaakkola, Shiyu Chang
Despite diffusion models' superior capabilities in modeling complex distributions, there are still non-trivial distributional discrepancies between generated and ground-truth images, which has resulted in several notable problems in image generation, including missing object errors in text-to-image generation and low image quality.
1 code implementation • 7 Dec 2023 • Bowen Jing, Tommi Jaakkola, Bonnie Berger
The runtime of our approach can be amortized at several levels of abstraction, and is particularly favorable for virtual screening settings with a common binding pocket.
1 code implementation • 5 Dec 2023 • John J. Yang, Jason Yim, Regina Barzilay, Tommi Jaakkola
Generating protein sequences that fold into a intended 3D structure is a fundamental step in de novo protein design.
no code implementations • 4 Dec 2023 • Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi Jaakkola
Adjustable hyperparameters of machine learning models typically impact various key trade-offs such as accuracy, fairness, robustness, or inference cost.
1 code implementation • 1 Dec 2023 • Chenyu Wang, Sharut Gupta, Caroline Uhler, Tommi Jaakkola
High-throughput drug screening -- using cell imaging or gene expression measurements as readouts of drug effect -- is a critical tool in biotechnology to assess and understand the relationship between the chemical structure and biological activity of a drug.
no code implementations • 20 Oct 2023 • Xiang Fu, Albert Musaelian, Anders Johansson, Tommi Jaakkola, Boris Kozinsky
When running MD, the MTS integrator then evaluates the smaller model for every time step and the larger model less frequently, accelerating simulation.
1 code implementation • 19 Oct 2023 • Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, Tommi Jaakkola
In light of the widespread success of generative models, a significant amount of research has gone into speeding up their sampling time.
no code implementations • 16 Oct 2023 • Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi Jaakkola, Jake Smith
Metal-organic frameworks (MOFs) are of immense interest in applications such as gas storage and carbon capture due to their exceptional porosity and tunable chemistry.
1 code implementation • 9 Oct 2023 • Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi Jaakkola
A significant amount of protein function requires binding small molecules, including enzymatic catalysis.
1 code implementation • 8 Oct 2023 • Jason Yim, Andrew Campbell, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Regina Barzilay, Tommi Jaakkola, Frank Noé
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching.
1 code implementation • 17 Jul 2023 • Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, Shuiwang Ji
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
1 code implementation • 2 Jul 2023 • Andrew Kirjner, Jason Yim, Raman Samusevich, Shahar Bracha, Tommi Jaakkola, Regina Barzilay, Ila Fiete
The ability to engineer novel proteins with higher fitness for a desired property would be revolutionary for biotechnology and medicine.
1 code implementation • NeurIPS 2023 • Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi Jaakkola
Restart not only outperforms the previous best SDE results, but also accelerates the sampling speed by 10-fold / 2-fold on CIFAR-10 / ImageNet $64 \times 64$.
1 code implementation • 6 Apr 2023 • Guanhua Zhang, Jiabao Ji, Yang Zhang, Mo Yu, Tommi Jaakkola, Shiyu Chang
COPAINT also uses the Bayesian framework to jointly modify both revealed and unrevealed regions, but approximates the posterior distribution in a way that allows the errors to gradually drop to zero throughout the denoising steps, thus strongly penalizing any mismatches with the reference image.
no code implementations • 5 Apr 2023 • Ziming Liu, Di Luo, Yilun Xu, Tommi Jaakkola, Max Tegmark
We introduce a general family, Generative Models from Physical Processes (GenPhys), where we translate partial differential equations (PDEs) describing physical processes to generative models.
1 code implementation • 5 Apr 2023 • Bowen Jing, Ezra Erives, Peter Pao-Huang, Gabriele Corso, Bonnie Berger, Tommi Jaakkola
Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensembles and flexibility that underlie biological function.
1 code implementation • 8 Feb 2023 • Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi Jaakkola
The new models reduce to PFGM when $D{=}1$ and to diffusion models when $D{\to}\infty$.
Ranked #1 on Image Generation on FFHQ 64x64 - 4x upscaling
1 code implementation • 5 Feb 2023 • Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi Jaakkola
The design of novel protein structures remains a challenge in protein engineering for applications across biomedicine and chemistry.
1 code implementation • 1 Feb 2023 • Yilun Xu, Shangyuan Tong, Tommi Jaakkola
We show that the procedure indeed helps in the challenging intermediate regime by reducing (the trace of) the covariance of training targets.
Ranked #12 on Image Generation on CIFAR-10
no code implementations • 28 Nov 2022 • Anurag Ajay, Yilun Du, Abhi Gupta, Joshua Tenenbaum, Tommi Jaakkola, Pulkit Agrawal
We further demonstrate the advantages of modeling policies as conditional diffusion models by considering two other conditioning variables: constraints and skills.
no code implementations • 14 Oct 2022 • Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi Jaakkola
Machine learning applications frequently come with multiple diverse objectives and constraints that can change over time.
1 code implementation • 13 Oct 2022 • Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, Tommi Jaakkola
We benchmark a collection of state-of-the-art (SOTA) ML FF models and illustrate, in particular, how the commonly benchmarked force accuracy is not well aligned with relevant simulation metrics.
2 code implementations • 4 Oct 2022 • Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi Jaakkola
We instead frame molecular docking as a generative modeling problem and develop DiffDock, a diffusion generative model over the non-Euclidean manifold of ligand poses.
Ranked #1 on Blind Docking on PDBbind
1 code implementation • 22 Sep 2022 • Yilun Xu, Ziming Liu, Max Tegmark, Tommi Jaakkola
We interpret the data points as electrical charges on the $z=0$ hyperplane in a space augmented with an additional dimension $z$, generating a high-dimensional electric field (the gradient of the solution to Poisson equation).
Ranked #31 on Image Generation on CIFAR-10
no code implementations • 25 Aug 2022 • Adam Fisch, Tommi Jaakkola, Regina Barzilay
Providing calibrated uncertainty estimates alongside predictions -- probabilities that correspond to true frequencies -- can be as important as having predictions that are simply accurate on average.
1 code implementation • 14 Jul 2022 • Wengong Jin, Regina Barzilay, Tommi Jaakkola
The binding affinity is governed by the 3D binding interface where antibody residues (paratope) closely interact with antigen residues (epitope).
1 code implementation • 8 Jun 2022 • Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi Jaakkola
Construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes.
1 code implementation • 1 Jun 2022 • Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, Tommi Jaakkola
Molecular conformer generation is a fundamental task in computational chemistry.
1 code implementation • 3 May 2022 • Bowen Jing, Gabriele Corso, Renato Berlinghieri, Tommi Jaakkola
Score-based models generate samples by mapping noise to data (and vice versa) via a high-dimensional diffusion process.
Ranked #19 on Image Generation on CIFAR-10
1 code implementation • 21 Apr 2022 • Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley D. Olsen, Tommi Jaakkola
Molecular dynamics (MD) simulation is essential for various scientific domains but computationally expensive.
1 code implementation • 15 Feb 2022 • Adam Fisch, Tal Schuster, Tommi Jaakkola, Regina Barzilay
We propose to trade coverage for a notion of precision by enforcing that the presence of incorrect candidates in the predicted conformal sets (i. e., the total number of false positives) is bounded according to a user-specified tolerance.
1 code implementation • 7 Feb 2022 • Hannes Stärk, Octavian-Eugen Ganea, Lagnajit Pattanaik, Regina Barzilay, Tommi Jaakkola
Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery.
Ranked #5 on Blind Docking on PDBBind
1 code implementation • ICLR 2022 • Yilun Xu, Hao He, Tianxiao Shen, Tommi Jaakkola
We propose to identify directions invariant to a given classifier so that these directions can be controlled in tasks such as style transfer.
1 code implementation • ICLR 2022 • Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi Jaakkola, Andreas Krause
Protein complex formation is a central problem in biology, being involved in most of the cell's processes, and essential for applications, e. g. drug design or protein engineering.
no code implementations • NeurIPS Workshop AI4Scien 2021 • Benson Chen, Xiang Fu, Regina Barzilay, Tommi Jaakkola
Equipped with the learned fragment vocabulary, we propose Fragment-based Sequential Translation (FaST), which learns a reinforcement learning (RL) policy to iteratively translate model-discovered molecules into increasingly novel molecules while satisfying desired properties.
2 code implementations • 19 Oct 2021 • Yilun Xu, Tommi Jaakkola
We further demonstrate the impact of optimizing such transfer risk on two controlled settings, each representing a different pattern of environment shift, as well as on two real-world datasets.
4 code implementations • ICLR 2022 • Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi Jaakkola
Generating the periodic structure of stable materials is a long-standing challenge for the material design community.
1 code implementation • ICLR 2022 • Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi Jaakkola
In this paper, we propose a generative model to automatically design the CDRs of antibodies with enhanced binding specificity or neutralization capabilities.
1 code implementation • 29 Jun 2021 • Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi Jaakkola
Current model-based reinforcement learning methods struggle when operating from complex visual scenes due to their inability to prioritize task-relevant features.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • CVPR 2021 • Karren Yang, Samuel Goldman, Wengong Jin, Alex X. Lu, Regina Barzilay, Tommi Jaakkola, Caroline Uhler
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development.
1 code implementation • EMNLP 2021 • Tal Schuster, Adam Fisch, Tommi Jaakkola, Regina Barzilay
In this work, we present CATs -- Confident Adaptive Transformers -- in which we simultaneously increase computational efficiency, while guaranteeing a specifiable degree of consistency with the original model with high confidence.
1 code implementation • 17 Feb 2021 • Adam Fisch, Tal Schuster, Tommi Jaakkola, Regina Barzilay
We develop a novel approach to conformal prediction when the target task has limited data available for training.
no code implementations • 9 Nov 2020 • Wengong Jin, Regina Barzilay, Tommi Jaakkola
Drug combinations play an important role in therapeutics due to its better efficacy and reduced toxicity.
1 code implementation • 28 Sep 2020 • Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi Jaakkola, Geoffrey Gordon, Stefanie Jegelka, Ruslan Salakhutdinov
While the advent of Graph Neural Networks (GNNs) has greatly improved node and graph representation learning in many applications, the neighborhood aggregation scheme exposes additional vulnerabilities to adversaries seeking to extract node-level information about sensitive attributes.
1 code implementation • ICLR 2021 • Adam Fisch, Tal Schuster, Tommi Jaakkola, Regina Barzilay
This set is guaranteed to contain a correct answer with high probability, and is well-suited for many open-ended classification tasks.
1 code implementation • 15 Jun 2020 • Karren Yang, Samuel Goldman, Wengong Jin, Alex Lu, Regina Barzilay, Tommi Jaakkola, Caroline Uhler
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development.
2 code implementations • 8 Jun 2020 • Benson Chen, Gary Bécigneul, Octavian-Eugen Ganea, Regina Barzilay, Tommi Jaakkola
Current graph neural network (GNN) architectures naively average or sum node embeddings into an aggregated graph representation -- potentially losing structural or semantic information.
Ranked #1 on Graph Regression on Lipophilicity (using extra training data)
no code implementations • 6 Jun 2020 • Wengong Jin, Regina Barzilay, Tommi Jaakkola
We evaluate our method on multiple applications: molecular property prediction, protein homology and stability prediction and show that RGM significantly outperforms previous state-of-the-art baselines.
no code implementations • 5 May 2020 • Wengong Jin, Regina Barzilay, Tommi Jaakkola
Effective property prediction methods can help accelerate the search for COVID-19 antivirals either through accurate in-silico screens or by effectively guiding on-going at-scale experimental efforts.
no code implementations • ICLR 2020 • Chen-Yu Hsu, Abbas Zeitoun, Guang-He Lee, Dina Katabi, Tommi Jaakkola
We show that this cross-modal prediction task allows us to detect when a particular appliance is used, and the location of the appliance in the home, all in a self-supervised manner, without any labeled data.
1 code implementation • 20 Feb 2020 • Shangyuan Tong, Timur Garipov, Tommi Jaakkola
We provide sufficient conditions for local convergence; characterize the capacity balance that should guide the discriminator and generator choices; and construct examples of minimally sufficient discriminators.
no code implementations • ICML 2020 • Vikas K. Garg, Stefanie Jegelka, Tommi Jaakkola
We address two fundamental questions about graph neural networks (GNNs).
2 code implementations • ICML 2020 • Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi Jaakkola
The property predictor is then used as a likelihood model for filtering candidate structures from the generative model.
4 code implementations • 8 Feb 2020 • Wengong Jin, Regina Barzilay, Tommi Jaakkola
These rationales are identified from molecules as substructures that are likely responsible for each property of interest.
1 code implementation • EMNLP 2020 • Tianxiao Shen, Victor Quach, Regina Barzilay, Tommi Jaakkola
We propose Blank Language Model (BLM), a model that generates sequences by dynamically creating and filling in blanks.
2 code implementations • ICML 2020 • Wengong Jin, Regina Barzilay, Tommi Jaakkola
Indeed, as we demonstrate, their performance degrades significantly for larger molecules.
1 code implementation • ICLR Workshop DeepGenStruct 2019 • John Ingraham, Vikas Garg, Regina Barzilay, Tommi Jaakkola
Engineered proteins offer the potential to solve many problems in biomedicine, energy, and materials science, but creating designs that succeed is difficult in practice.
1 code implementation • NeurIPS 2019 • Guy Lorberbom, Tommi Jaakkola, Andreea Gane, Tamir Hazan
Reparameterization of variational auto-encoders with continuous random variables is an effective method for reducing the variance of their gradient estimates.
no code implementations • 25 Sep 2019 • Tianxiao Shen, Jonas Mueller, Regina Barzilay, Tommi Jaakkola
Neural language models have recently shown impressive gains in unconditional text generation, but controllable generation and manipulation of text remain challenging.
no code implementations • 25 Sep 2019 • Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi Jaakkola
Many challenging prediction problems, from molecular optimization to program synthesis, involve creating complex structured objects as outputs.
1 code implementation • 11 Jun 2019 • Wengong Jin, Regina Barzilay, Tommi Jaakkola
The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties.
Ranked #1 on Drug Discovery on QED
no code implementations • NeurIPS 2019 • Vikas K. Garg, Tommi Jaakkola
The transport problem is seeded with prior information about node importance, attributes, and edges in the graph.
3 code implementations • ICML 2020 • Tianxiao Shen, Jonas Mueller, Regina Barzilay, Tommi Jaakkola
We prove that this simple modification guides the latent space geometry of the resulting model by encouraging the encoder to map similar texts to similar latent representations.
2 code implementations • 29 May 2019 • Benson Chen, Regina Barzilay, Tommi Jaakkola
Much of the recent work on learning molecular representations has been based on Graph Convolution Networks (GCN).
no code implementations • 29 May 2019 • Vikas K. Garg, Tommi Jaakkola
We introduce a new class of context dependent, incomplete information games to serve as structured prediction models for settings with significant strategic interactions.
no code implementations • ICLR 2019 • Paresh Malalur, Tommi Jaakkola
An attention mechanism can be used to highlight the area of the image that the model focuses on thus offering a narrow view into the mechanism of classification.
no code implementations • ICLR 2019 • Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola
We evaluate our model on multiple molecule optimization tasks and show that our model outperforms previous state-of-the-art baselines by a significant margin.
4 code implementations • 2 Apr 2019 • Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi Jaakkola, Klavs Jensen, Regina Barzilay
In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary datasets.
Ranked #3 on Molecular Property Prediction on QM9
no code implementations • 11 Mar 2019 • Paresh Malalur, Tommi Jaakkola
An attention mechanism can be used to highlight the area of the image that the model focuses on thus offering a narrow view into the mechanism of classification.
5 code implementations • 3 Dec 2018 • Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola
We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines.
2 code implementations • NeurIPS 2018 • David Alvarez Melis, Tommi Jaakkola
Most recent work on interpretability of complex machine learning models has focused on estimating a-posteriori explanations for previously trained models around specific predictions.
1 code implementation • 24 Jul 2018 • Luke B. Hewitt, Maxwell I. Nye, Andreea Gane, Tommi Jaakkola, Joshua B. Tenenbaum
However, when this generative model is expressed as a powerful neural network such as a PixelCNN, we show that existing learning techniques typically fail to effectively use latent variables.
2 code implementations • ICLR 2019 • Guy Lorberbom, Andreea Gane, Tommi Jaakkola, Tamir Hazan
We demonstrate empirically the effectiveness of the direct loss minimization technique in variational autoencoders with both unstructured and structured discrete latent variables.
11 code implementations • ICML 2018 • Wengong Jin, Regina Barzilay, Tommi Jaakkola
We evaluate our model on multiple tasks ranging from molecular generation to optimization.
Ranked #1 on Molecular Graph Generation on InterBioScreen
no code implementations • ICLR 2018 • Benson Chen, Connor Coley, Regina Barzilay, Tommi Jaakkola
Deep learning algorithms are increasingly used in modeling chemical processes.
no code implementations • ICLR 2018 • Luke Hewitt, Andrea Gane, Tommi Jaakkola, Joshua B. Tenenbaum
Hierarchical Bayesian methods have the potential to unify many related tasks (e. g. k-shot classification, conditional, and unconditional generation) by framing each as inference within a single generative model.
no code implementations • NeurIPS 2017 • Vikas Garg, Tommi Jaakkola
Aggregative games provide a rich abstraction to model strategic multi-agent interactions.
1 code implementation • NeurIPS 2017 • Wengong Jin, Connor W. Coley, Regina Barzilay, Tommi Jaakkola
The prediction of organic reaction outcomes is a fundamental problem in computational chemistry.
no code implementations • ICML 2017 • Jonas Mueller, David Gifford, Tommi Jaakkola
Under this model, gradient methods can be used to efficiently optimize the continuous latent factors with respect to inferred outcomes.
1 code implementation • 1 Aug 2017 • Karthik Narasimhan, Regina Barzilay, Tommi Jaakkola
In this paper, we explore the utilization of natural language to drive transfer for reinforcement learning (RL).
12 code implementations • NeurIPS 2017 • Tianxiao Shen, Tao Lei, Regina Barzilay, Tommi Jaakkola
We demonstrate the effectiveness of this cross-alignment method on three tasks: sentiment modification, decipherment of word substitution ciphers, and recovery of word order.
Ranked #7 on Text Style Transfer on Yelp Review Dataset (Small)
no code implementations • ICML 2017 • Tao Lei, Wengong Jin, Regina Barzilay, Tommi Jaakkola
The design of neural architectures for structured objects is typically guided by experimental insights rather than a formal process.
1 code implementation • TACL 2017 • Yuan Zhang, Regina Barzilay, Tommi Jaakkola
We introduce a neural method for transfer learning between two (source and target) classification tasks or aspects over the same domain.
no code implementations • NeurIPS 2016 • Vikas Garg, Tommi Jaakkola
Many real phenomena, including behaviors, involve strategic interactions that can be learned from data.
no code implementations • 16 Jun 2016 • Jonas Mueller, David N. Reshef, George Du, Tommi Jaakkola
Assuming the underlying relationship remains invariant under intervention, we develop efficient algorithms to identify the optimal intervention policy from limited data and provide theoretical guarantees for our approach in a Gaussian Process setting.
3 code implementations • EMNLP 2016 • Tao Lei, Regina Barzilay, Tommi Jaakkola
Our approach combines two modular components, generator and encoder, which are trained to operate well together.
no code implementations • 10 Feb 2016 • Tamir Hazan, Francesco Orabona, Anand D. Sarwate, Subhransu Maji, Tommi Jaakkola
This paper shows that the expected value of perturb-max inference with low dimensional perturbations can be used sequentially to generate unbiased samples from the Gibbs distribution.
1 code implementation • NAACL 2016 • Tao Lei, Hrishikesh Joshi, Regina Barzilay, Tommi Jaakkola, Katerina Tymoshenko, Alessandro Moschitti, Lluis Marquez
Question answering forums are rapidly growing in size with no effective automated ability to refer to and reuse answers already available for previous posted questions.
no code implementations • NeurIPS 2015 • Jonas Mueller, Tommi Jaakkola
We introduce principal differences analysis (PDA) for analyzing differences between high-dimensional distributions.
no code implementations • 20 Aug 2015 • Tamir Hazan, Tommi Jaakkola
Contemporary deep neural networks exhibit impressive results on practical problems.
2 code implementations • EMNLP 2015 • Tao Lei, Regina Barzilay, Tommi Jaakkola
Moreover, we extend the n-gram convolution to non-consecutive words to recognize patterns with intervening words.
no code implementations • 5 Aug 2015 • Jean Honorio, Tommi Jaakkola
Thus, using the maximum loss over random structured outputs is a principled way of learning the parameter of structured prediction models.
no code implementations • 25 Jun 2015 • Vikas K. Garg, Cynthia Rudin, Tommi Jaakkola
We present a framework for clustering with cluster-specific feature selection.
1 code implementation • TACL 2015 • Karthik Narasimhan, Regina Barzilay, Tommi Jaakkola
In contrast, we propose a model for unsupervised morphological analysis that integrates orthographic and semantic views of words.
no code implementations • NeurIPS 2014 • Yu Xin, Tommi Jaakkola
Recommender systems involve an inherent trade-off between accuracy of recommendations and the extent to which users are willing to release information about their preferences.
no code implementations • NeurIPS 2013 • Tamir Hazan, Subhransu Maji, Joseph Keshet, Tommi Jaakkola
In this work we develop efficient methods for learning random MAP predictors for structured label problems.
no code implementations • 15 Oct 2013 • Francesco Orabona, Tamir Hazan, Anand D. Sarwate, Tommi Jaakkola
Applying the general result to MAP perturbations can yield a more efficient algorithm to approximate sampling from the Gibbs distribution.
no code implementations • NeurIPS 2013 • Tamir Hazan, Subhransu Maji, Tommi Jaakkola
In this paper we describe how MAP inference can be used to sample efficiently from Gibbs distributions.
no code implementations • 25 Aug 2012 • Fahiem Bacchus, Tommi Jaakkola
This is the Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence, which was held in Edinburgh, Scotland July 26 - 29 2005.
no code implementations • 18 Jul 2012 • Jean Honorio, Tommi Jaakkola, Dimitris Samaras
In this paper, we present $\ell_{1, p}$ multi-task structure learning for Gaussian graphical models.